Abstract: The development of big data in the era of data explosion and the growing demand for micro-servers in place of traditional servers to adapt to lightweight tasks in recent years has put into question how to integrate and make use of these two important domains. During the same era, CPU performance growth has reached a certain maturity. In order to surpass these issues and to reach high performances computing, a new trend now is to use multiple processing units or heterogeneous components in micro-servers to reduce computational complexity. The implementation of Big Data processing algorithms using embedded heterogeneous architectures rises a new challenges due to constraints of the used architecture-based system on chip which require a special attention and imposed new demands to our works. In this article, we focus on using embedded FPGA accelerator to give a solution to this problem. Precisely, we will attempt to prototype a micro-server for the processing of big data on FPGA and compare its performances with a high-end GPGPU using existing benchmarks. The implementation on the FPGA is done using a High-Level Synthesis based-OpenCL (HLS) instead of the traditional description language. The obtained results shows that FPGA is an interesting alternative and can be a promising platform to design a micro-server when it comes to process a hug amount of data, in particular with the emerging technologies for FPGA programming using HLS approach and by adopting the OpenCL optimization strategies.

Mohamed Abouzahir, Khalifa Elmansouri, Rachid Latif and Mustapha Ramzi, “Towards a Low-Cost FPGA Micro-Server for Big Data Processing” International Journal of Advanced Computer Science and Applications(IJACSA), 13(1), 2022. http://dx.doi.org/10.14569/IJACSA.2022.01301101

@article{Abouzahir2022,
title = {Towards a Low-Cost FPGA Micro-Server for Big Data Processing},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.01301101},
url = {http://dx.doi.org/10.14569/IJACSA.2022.01301101},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {1},
author = {Mohamed Abouzahir and Khalifa Elmansouri and Rachid Latif and Mustapha Ramzi}
}